Experiential-Informed Data Reconstruction for Fishery Sustainability and Policies in the Azores

TL;DR

This paper presents a machine learning approach to reconstruct missing fishery data related to fishing gear types in the Azores, enabling better assessment and management of fish populations and marine ecosystems.

Contribution

It introduces a novel data reconstruction method combining domain knowledge and machine learning to recover missing fishery metadata, improving sustainability analysis.

Findings

Reconstruction of metier data is feasible with diverse modeling approaches.

Enhanced understanding of fisheries' behavior and impact over time.

Supports improved fishery management and conservation strategies.

Abstract

Fishery analysis is critical in maintaining the long-term sustainability of species and the livelihoods of millions of people who depend on fishing for food and income. The fishing gear, or metier, is a key factor significantly impacting marine habitats, selectively targeting species and fish sizes. Analysis of commercial catches or landings by metier in fishery stock assessment and management is crucial, providing robust estimates of fishing efforts and their impact on marine ecosystems. In this paper, we focus on a unique data set from the Azores' fishing data collection programs between 2010 and 2017, where little information on metiers is available and sparse throughout our timeline. Our main objective is to tackle the task of data set reconstruction, leveraging domain knowledge and machine learning methods to retrieve or associate metier-related information to each fish landing. We empirically validate the feasibility of this task using a diverse set of modeling approaches and demonstrate how it provides new insights into different fisheries' behavior and the impact of metiers over time, which are essential for future fish population assessments, management, and conservation efforts.